ObjectGoal navigation. Recent work on end-to-end RL for ObjectNav has proposed improved visual representations [1]}, [2]}, auxiliary tasks [3]}, and data augmentation techniques [4]} to improve generalization to novel scenes. Improved visual representations include semantic segmentations [1]}, spatial attention maps [6]}, and object relation graphs [2]}, [8]}, [9]}, [10]}, [11]}, [12]}. Prior work also learns auxiliary tasks such as predicting agent dynamics, environment states, and map coverage simultaneously with ObjectNav and achieves promising results [3]}. Most recently, treasure hunt data augmentation [4]} achieved state-of-the-art on ObjectNav by training with artificially inserted objects and improving the RL reward [4]}.
